System and method for monitoring the health of dialysis patients

ABSTRACT

A system and method for monitoring the health of dialysis patients with Raman spectroscopy measurements of one or more target analytes is described. The methods include irradiating one or more fluids of interest with light to produce one or more spectrum and detecting the spectrum with a detector. The fluids of interest are preferably those related to dialysis, including hemodialysis and peritoneal dialysis. In a preferred embodiment, the fluids are irradiated with monochromatic light, and one or more Raman spectra are detected as a result of the irradiation. The fluids may be irradiated within the dialysis tubing itself, or removed from the dialysis tubing and irradiated in a separate chamber. The Raman spectra of one or more target analytes of a dialysis patient may be followed over time or compared to one or more reference spectra, thereby providing information on the health of dialysis patients.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relies on the disclosure of and claims priority to andthe benefit of the filing date of U.S. Provisional Application No.61/983,038, filed on Apr. 23, 2014, the disclosure of which is herebyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to the field of clinical care of dialysisand kidney transplant patients. More particularly, the present inventionrelates to a system and method for monitoring the health of dialysis andkidney transplant patients with Raman spectroscopy measurements of oneor more target analytes.

Description of Related Art

Kidney disease is prevalent and can lead to both acute and chronic renalfailure and to end-stage renal disease (ESRD). The Centers for DiseaseControl and Prevention estimates that one in 10 Americans (more than 20million people) are affected to some degree with chronic renal failure(National Kidney and Urologic Disease Information Clearinghouse,National Institutes of Diabetes, Digestive, and Kidney Diseases[NIDDK]). There is very high morbidity and mortality from ESRD. To putthis in perspective, many of the more common forms of cancer have abetter prospect for survival than ESRD. In addition, ESRD is a globalproblem (“ESRD patients in 2011: A global perspective,” FreseniusMedical Corporation GmBH).

There are many causes of kidney disease, but some of the most commoninclude inflammation (glomerulonephritis, pyelonephritis),cardiovascular disease, hypertension (high blood pressure), and diabetesmellitus (Centers for Disease Control and Prevention, National ChronicKidney Disease Factsheet, 2014). Many kidney diseases are progressivediseases, beginning with relatively mild symptoms and manageableconsequences that, with time, significantly damage and scar the kidneys.As people age, they experience more chronic kidney disease. With acontinuing increase in the proportion of the elderly of the populationsof all countries, the prevalence of chronic kidney disease will rise.

Patients who are in renal failure, due to acute disease or ESRD, requireintensive medical therapy. Some cases of acute renal failure can bemedically managed, but virtually all patients with ESRD require eitherdialysis or transplantation of a kidney to live. Worldwide (2011 data),it is estimated that there were 2,164,000 ESRD patients being treatedwith either hemodialysis (HD) or peritoneal dialysis (PD) (“ESRDpatients in 2011: A global perspective,” Fresenius Medical CorporationGmBH). Fresenius Medical Corporation estimates that the number of ESRDpatients increases approximately 1-4% annually, due primarily to threefactors: overall population growth, increase in lifespan due to old age(ESRD is more common), and increased incidence of diseases that causeESRD (especially diabetes and hypertension) (“ESRD patients in 2011: Aglobal perspective,” Fresenius Medical Corporation GmBH).

In healthy people with normal renal function, the kidneys constantlyremove a variety of metabolic waste products from the circulation.Examples of some waste product molecules are urea, formed duringmetabolism of proteins and amino acids, and creatinine, formed duringremodeling of skeletal muscle cells. In fact, there are hundreds ofwaste products that are excreted through the kidney (and also thedigestive tract). In people with renal failure, these waste productscannot be efficiently excreted, and they accumulate in the circulation,producing clinical signs of illness. The clinical syndrome “uremia”(literally “urine in the blood”) is diagnosed when there is retention ofexcessive nitrogenous wastes in the circulation and other abnormalitiesrelated to renal failure, including dystrophic/metastatic calcificationof soft tissues and demineralizing osteopathy (related to poor controlof dietary calcium absorption and skeletal demineralization), anemia(the kidney makes the hormone erythropoietin), and anorexia, gastritis,gastric ulceration, pruritis/neuropathy, and pericarditis.

Some retained (unfiltered or unsecreted) waste molecules arecollectively referred to as “uremic toxins.” More than 80 molecules havebeen identified that as potential uremic toxins, which contribute tosystemic morbidity associated with renal failure (Dobre, M, Meyer, T W,Hostetter, T H, “Searching for uremic toxins,” Clin J Am Soc Nephrol 8:322327, 2013 (“Dobre et al., 2013”); Liabeuf, S, Drukke, T B, Massay, ZA, “Protein-bound uremic toxins: new insight from clinical studies,”Toxins 3: 911-919, 2011 doi:10.3390/toxins3070911; Ito, S, Yoshida, M,“Review: Protein-bound uremic toxins: new culprits of cardiovascularevents in chronic kidney disease patients,” Toxins 6: 665-678, 2014;doi:10.3390/toxins6020665; and Duranton, F, Cohen, G, De Smet, R,Rodgiquez, M, Jankowski, J, Vanholder, R, Argiles, A, “Normal andpathological concentrations of uremic toxins,” J Am Soc Nephrol 23:1258-1270, 2012. doi: 10.1681/ASN.2011121175 (“Duranton et al., 2012”).Many of these molecules are not removed well by either hemodialysis orperitoneal dialysis.

The primary goals of dialysis are reduction in the amount of circulatingsmall molecular weight metabolic waste products, correction of plasmahydration, and systemic ion balancing (electrolytes, H+, HCO3⁻,phosphate, among others). Reductions in small solute waste molecules andadjustment of the concentrations of water, ions, and pH, while nottrivial, are usually accomplished well with current dialyzer membranes.These are small molecules and their movement between blood and dialysateis relatively predictable and controllable, based on flow, membranecharacteristics, and duration/intensity of dialysis.

Hemodialysis (HD) is a therapeutic medical process during whichmetabolic impurities, such as the nitrogenous waste products of proteindigestion and metabolism, are removed from the body of patients withrenal failure. This is accomplished by selective filtration of plasmathrough semipermeable, engineered polymeric membranes. For HD treatment,the circulation of the patient is connected to a dialysis machine by wayof an access cannula, usually in the patient's arm. Patient bloodpressure, as well as a mechanical pump in the HD machine, circulatespatient blood past a selectively porous membrane (“the dialyzer” or“coil”). The porous membrane of the dialyzer facilitates removal ofsmall molecules, such as water, electrolytes, and small molecular weightnitrogenous wastes, primarily by processes of diffusion and osmosis. Theblood of the patient remains on one side of the porous membrane and anaqueous fluid (“the dialysate”) circulates on the other side.Concentration differences between molecules in the patient's circulation(high concentration of metabolic nitrogenous wastes, for example) andthe dialysate (no nitrogenous wastes at the beginning of the cycle)osmotically draw waste products from the patient into the dialysate. The“patient contaminated” dialysate is constantly being discarded andreplaced with fresh dialysate, providing the gradual removal ofundesirable molecules from the circulation. HD patients graduallyaccumulate more metabolic wastes in their circulation over several days,requiring an HD treatment for several hours every few days. A typical HDpatient is usually treated three or more times weekly, with 3-5 hoursper treatment. A schematic simplified diagram of HD is shown in FIG. 1.

A second type of dialysis is peritoneal dialysis. In this type oftreatment, a modified dialysate is infused into the peritoneal(abdominal) cavity of the patient with ESRD through a transabdominal,selectively porous catheter. Osmotic exchange of waste metabolites, suchas urea, occurs across the thin membranes that cover the intestines andperitoneum. Periodically, the used dialysis fluids, with the dissolvedwaste metabolites, are drained and discarded.

Dialysis is a life-sustaining, temporary, maintenance medical therapy.It does not replace renal function and does not provide long-termpatient survival. In reality, HD patients rarely survive more than 10years and fewer than 40% survive more than 5 years. It is discouragingto note that the process of dialysis itself is dangerous andlife-threatening. In virtually every age group studied, patients whoundergo dialysis are more at risk of death than ESRD patients alone (nottreated) or those treated with renal transplantation (Fink, J C,“Current outcomes for dialysis patients and improving quality of carefor dialysis patients,” in Principles and Practice of Dialysis, 4th ed.,Henrich, W I (ed.) Wolters Kluwer/Lippincott, Williams and Wilkins,Philadelphia, 2009) (“Fink, 2009”). In this context, dialysis treatmentis associated with higher mortality than medical management of ESRD.These data may not be completely representative, since dialysispatients, on average, may be more ill (thus requiring intensive medicalintervention) than medically-managed ESRD patients.

The major cause of death in dialysis patients is cardiovascular disease.This includes death from sudden, catastrophic heart failure, myocardialinfarction, cerebrovascular events, and segmental/diffuse cardiomyopathy(Levey A S, Beto J A, Coronado B E, Eknoyan G, Foley R N, Kasiske B I,Klag M J, Mailloux I U, Manske C I, Meyer K B, Parfrey P S, Pfeffer M A,Wenger N K, Wilson P W, Wright J T Jr.levey, A, Beto, J A, “Controllingthe epidemic of cardiovascular disease in chronic renal disease: What dowe need to learn? Where do we go from here?,” Amer J Kid Disease 32:853-908, 1998; and Collins, A J, Li, S, St Peter, W, Ebben J, Roberts,T, Ma, J Z, Manning, W, “Death, hospitalization, and economicassociations among incident hemodialysis patients with hematocrit valuesof 36 to 39%,” J Am Soc Nephrol 12(11):2465-73, 2001). Significantcomplications suffered directly as a result of the dialysis process (andindirectly from ESRD and co-morbidities causing ESRD) are anemia,skeletal demineralization and predisposition to pathologic fractures,failure/infection at the site of arteriovenous access, hemostaticabnormalities, abnormalities of drug effects/clearances, dyslipidemia,and altered acid-base homeostasis.

Taken together, this information indicates that although it istheoretically possible to maintain patients on dialysis for decades, inreality this does not occur. There could be many reasons for thedisjunction between theoretical benefits of dialysis and the realitiesof outcomes (especially life expectancy), including that waste productsof metabolism, that are detrimental to physiologic function, are notbeing sufficiently managed/cleared by current technology (“uremictoxins”); current methods for determining the dialysis needs of eachpatient (urea clearance—see below) may not accurately predict the actualdialysis needs of every patient; inadequate management of co-morbiditiessuch as diabetes mellitus and hypertension; complications associatedwith, and failure of, of arteriovenous access; and/or complications ofdialysis that alter patient physiology, including acid-base balance,hydration, ion trafficking, damage to formed elements of blood, andaccumulation of undesirable ions from dialysates in tissues, amongothers.

No two patients receiving dialysis therapy are the same. They may differin the degree of renal failure they are experiencing (some patientsstill have a small amount of residual function from their own kidneys),their metabolism and endogenous rate of generation of metabolic wastes,co-morbidities that affect metabolism and waste generation (diabetes orheart disease, for example), and even their water intake and diet (whichaffect metabolism and waste product generation). This is an importantpoint, discussed further below.

Despite the fact that no two HD patients are the same, virtually all HDpatients receive the same treatment—HD ‘sessions’ about 3× weekly forseveral hours per session. This ‘prescription’ for HD therapy was basedon an analysis of patient data included in The National CooperativeDialysis Study in the 1970s and the NIH HEMO Study, summarized byEknoyan, et. al. (Eknoyan, G, Beck, G J, Cheung, A K, Daugirdas, J T,Greene, T, Kusek, J W, Allon, M, Bailey, J, Delmez, J A, Depner, T A,Dwyer, J T, Levey, A S, Levin, N W, Milford, E, Ornt, D B, Rocco, M V,Schulman, G, Schwab, S J, Teehan, B P, Toto, R, “Effects of dialysisdose and membrane flux in maintenance hemodialysis,” New Engl Journ Med347: 2010-2019, 2002). The amount (“prescription”) of dialysis treatmentis based almost entirely on kinetic modeling of small solute (urea)distribution and extraction during dialysis (Depner, TA, “Approach tohemodialysis kinetic modeling,” in Principles and Practice of Dialysis.4^(th) ed., Henrich, W L (ed.) Wolters Kluwer/Lippincott, Williams andWilkins, Philadelphia, 2009). The success of treatment is closelymonitored and adjusted accordingly (see below). During each HD session,patients are weighed (in order to assess the rate and volume of wateraccumulation from the dialysate into the patient), and have constantmonitoring of blood pressure (blood pressure can be quite labile) andbody temperature (despite warming of dialysate and patient, hypothermiais common). PD patients may be treated several times weekly, withprescription of therapy varying among individuals. The success oftreatment of PD patients is generally based on serum creatinine values,metabolic state and quality of life.

Laboratory and physical examinations on each patient occur frequently.Each week (for the typical ‘stable’ patient), a complete blood count isperformed, and each month a complete serum chemistry profile isanalyzed. Each month, the urea reduction rate is calculated. Every 3-6months, the lipid profile of each patient is assessed (dyslipidemia is acommon problem in HD patients) and they are screened for the presence ofhepatitis virus infections (to prevent cross-contamination of shared-usedialysis machines and as to protect dialysis center personnel frominadvertent exposure). If patients do not appear to be respondingpredictably to therapy or if emergent co-morbidities are changingoverall clinical status, more frequent testing and adjustment of HD orPD conditions is done (Personal communication. H J Ballenger and R DJarrett to J L Robertson, Apr. 5, 2014).

Urea, a small molecule (60 Daltons) normally generated as a byproduct ofprotein and amino acid metabolism, is used as a marker of small soluteclearance from plasma to dialysate during HD (termed the “urea reductionrate—URR”). Since urea is a byproduct of protein metabolism, it isproduced in the liver during digestion and rises and falls withconsumption and processing of dietary protein and during some catabolicstates. Urea is considered to be a normal product of metabolism and tobe relatively non-toxic, i.e., not a significant contributor to clinicalsymptoms of uremia and disease progression in ESRD patients (Merrill, JP, Legrain, M, Hoigne, R, “Observations on the role of urea in uremia,”Amer J Med 14: 519520, 1953). Physico-chemical characteristics of ureamake it a suitable marker for dialysis efficacy. As a small moleculethat is readily dissolved in the aqueous component of plasma, urea isreadily and constantly filtered from plasma through intact nephrons inhealthy kidneys. Urea is electrically neutral and crosses intact cellmembranes by diffusion and facilitated transport. The movement of ureain and out of red cells is important; red cell membranes effectivelytransport urea and act to compartmentalize it. Other small solutemolecules, such as phosphate and creatinine do not transit in red cellsand this affects their distribution and clearance.

In patients with renal failure, there are insufficient intact remnantnephrons to remove urea (which is being intermittently generated bymetabolism). With intermittent/constant production, but insufficientremoval, urea levels in plasma (“blood urea nitrogen—BUN”) rise,indicating renal dysfunction and reduced nephron mass. In a healthyperson, with well-functioning kidneys and sufficient nephron mass, thevalue of blood urea nitrogen is 10-20 mg/dl. Values above these aresuggestive of renal disease, but can be affected by state of hydration,dietary protein intake, digestion, and several other factors.

Patients with untreated ESRD commonly have BUN values >50-100 mg/dl.When a patient receives a dialysis treatment, plasma urea diffusesacross the dialyzer membrane, driven by osmotic forces (low ureaconcentration in dialysate fluid), and is then removed as dialysate isdrained and discarded. The efficiency of urea extraction is a functionof type of dialyzer membrane, flow of blood and dialysate across theopposing sides of the membrane, treatment duration and frequency, andpatient parameters (blood pressure, hydration (total body water and ureadistribution/solute compartmentalization), state of nutrition and rateof urea generation, and residual nephron mass).

For each patient, computations of urea extraction, assessment of proteincatabolism, and clinical signs indicative of proper management determinethe dialysis prescription. Nephrologists adjust the prescription upwards(more frequent or intensive dialysis) if the patient is not stable ordeteriorating. Most nephrologists acknowledge that while urea is usefulin defining the prescription, it does not define the uremic state (oraccurately reflect the many factors affecting protein metabolism).

Hemodialysis (HD) and peritoneal dialysis do not replace lost renalfunction. Many metabolic waste products are either too large,stereooptically-hindered, or electrostatically charged, to pass throughdialyzer membranes, are bound to plasma proteins which preventfiltration, or are distributed within tissue fluid and metaboliccompartments which limit exchange and equilibration with plasma. Somewaste products, such as creatinine and hippurate, are partiallyeliminated in healthy people (with normal renal function) by secretionfrom cells in intact nephron segments. With the typical profound loss ofintact nephrons in patients with ESRD, these secretory activities do notoccur (Dobre et al., 2013). Shannon (Shannon J A, “Renal tubularexcretion,” Physiol Rev 19: 63-93, 1939) noted, in 1939, that there maybe many solutes produced and regulated by normal kidneys that we havenot even identified or measured. The loss of nephron mass undoubtedlyaffects their clearance—but we do not know what they are or what itmeans to have them accumulate in ESRD patients. The loss of clearance inESRD (and not met with HD or PD) may affect the development of uremiaand clinical progression of ESRD. This is an important point, discussedfurther below.

Because it is fundamentally designed to remove small solute molecules,and is relatively non-selective in doing so, HD and PD may removenutrients (vitamin C, folic acid, vitamin B12, zinc, and amino acids,for example) that are needed for physiologic functions. Thisnon-selective loss of metabolically-important molecules may contributeto morbidity seen in most HD patients (Dobre et al., 2013; and Kopple, JD, Kalantar-Zadeh, K, “Malnutrition and intradialytic parenteralnutrition in patients with end-stage renal disease,” in Principles andPractice of Dialysis. 4th ed., Henrich, W L (ed.) WoltersKluwer/Lippincott, Williams and Wilkins, Philadelphia, 2009)).Currently, there is no routine monitoring of “desirable molecule loss”in HD.

Uremic toxins are molecules that are normally removed from thecirculation by the kidney, but that accumulate in the fluids and tissuesof patients with ESRD. The molecules, also referred to as uremicretention solutes, adversely affect a variety of physiologic functions,and are associated with morbidity and mortality (Duranton et al., 2012;and Meyer, T W, Hostetter, T H, “Uremia”, New Engl J Med 357: 1316-1325,2007; Vanholder, R, De Smet, R, Glorieux, G, Argiles, A, Baurmeister, U,Brunet, P, Clark, W, Cohen, G, De Deyn, P P, Deppisch, R,Descamps-Latscha, B, Henle, T, J6rres A, Lemke, H D, Mass,y Z A,Passlick-Deetjen, J, Rodriguez, M, Stegmayr, B, Stenvinkel, P, Tetta, C,Wanner, C, Zidek, W; European Uremic Toxin Work Group (EUTox), “Reviewon uremic toxins: classification, concentration, and interindividualvariability,” Kidney Int. 2003 May; 63(5):1934-43, 2003). There is broadconsensus that uremic toxins are not effectively removed by either formof dialysis; uremic toxins materially contribute to the perpetualillness, medical fragility, and decreased lifespan of HD patients; thetype, concentration, kinetics of metabolism, and fluctuations incirculating levels of uremic toxins is highly variable among HDpatients; it is not known which toxins or combinations of toxins areresponsible for uremia; all toxins have not been clearly identified; andmeasurement of uremic toxins is not done dynamically, at the point ofcare, for individual patients; instead, these measurements andassociated correlations are largely in the domain of research studies,done in academic centers, with sophisticated and costly analyticalequipment and methodology.

There are three primary classes of uremic toxins, classified primarilyon molecular mass and binding properties (with plasma components). In acomprehensive recent review of eighty-eight uremic toxins, Duranton,et.al. (Duranton et al., 2012) noted the following distribution ofcurrently identified molecules within these classes: free water soluble,low molecular weight molecules <<0.5 kD) 40/88 (46%); middle molecules,variable water solubility (0.5 kD-60 kD) 25/88 (28%); and protein-boundmolecules 23/88 (25%). The methodology used to identify the 88 uremictoxins includes: ion exchange chromatography; gas chromatography and gaschromatography/mass spectroscopy; high performance liquidchromatography; spectrophotometry, nephelometry, fluorometry;chemiluminescence; radioimmunometry; and nuclear magnetic resonancespectroscopy.

The pace of discovering new uremic toxins is brisk. Comparing acomprehensive list of uremic toxins from the EUTox (European UremicToxin Work Group) in 2003, with an updated list from 2012, there arefifty-five new toxins on the most recent list (a 175% increase in knownor suspected uremic toxins in just 8 years). Data in the most recentreview/listing (Duranton et al., 2012) indicated that increasedsensitivity of assay methods adjusted the concentration upward (moreretention in ESRD patients on HD) for several molecules. Theconcentrations of some molecules (indole-3-acetic acid, free/totalindoxyl sulfate, free/total p-cresyl sulfate, uric acid) were felt to beunderestimated by current analytic methods, based on comparisons ofmultiple analytical methods and reported values.

Information presented by Duranton, et. al, (Duranton et al., 2012)showed significant variation of many molecules among individual patients(termed “high” and “low” concentration molecules). These includedcarboxymethyllysine, free indoxyl sulfate, and phenol-all molecules thatare protein bound. Several middle molecules showed similarly highindividual variation, including parathyroid hormone (PTH) fragments,TNFa, leptin, osteocalcin, and interleukin-8.

Some molecules are actually present in lower concentrations in HDpatients than in patients not receiving HD. These molecules includebilirubin, reduced glutathione, a1-anti-trypsin, arginine, andhomoarginine. Biological functions of these molecules includevasodilation, free radical quenching/scavenging, and control ofinflammation. Reduction of normal physiological concentrations of thesemolecules likely has some role in the morbidity associated with uremiaand ESRD.

Thirty-two uremic toxins identified in the 2003 EUTox data (Vanholder,R, De Smet, R, Glorieux, G, Argiles, A, Baurmeister, U, Brunet, P,Clark, W, Cohen, G, DeDeyn, P P, Deppisch, R, Descamps-Latscha, B,Henle, T, J6rres A, Lemke, H D, Mass,y Z A, Passlick-Deetjen, J,Rodriguez, M, Stegmayr, B, Stenvinkel, P, Tetta, C, Wanner, C, Zidek, W;European Uremic Toxin Work Group (EUTox), “Review on uremic toxins:classification, concentration, and interindividual variability,” KidneyInt. 2003 May; 63(5):1934-43, 2003) were found to be present at lowerconcentrations in patients dialyzed with current, improved methodology.This data suggests that new technologies and more aggressive HD use maysignificantly affect the concentrations of uremic toxins in thecirculation of HD patients. From this information, it is abundantlyclear that many molecules reflecting both health and disease are presentin blood, plasma, urine, and, importantly, in the waste dialysis streamof hemodialysis equipment.

The present inventors have shown by analysis of dialysate waste streamsamples that the composition of the dialysate waste stream changes fromthe beginning to the end of the dialysis cycle in terms of both thetypes and concentrations of dialyzable molecules present. These changesrepresent the kinetic efficiency of the dialysis process. Such changesare not currently and routinely monitored by any dialysis system. Theseimportant and unique observations can be used to assess the efficacy ofthe dialysis process and dialysis equipment.

Unless improvements are made in the process of dialysis (more efficientmachines that more effectively mimic renal excretory functions, moreeffective application of individualized patient “prescriptions”),patients with ESRD are doomed to ongoing illness and shortenedlifespans. Current laboratory methods to assess dialysis efficacy do notallow dynamic assessment of patients during and between therapies andare not designed to assess the kinetic role of uremic toxins in diseaseprogression.

Efforts in this area and related areas include those described in U.S.Pat. Nos. 7,326,576, 8,945,936, 8,953,159, U.S. Patent ApplicationPublication No. 20080097272, and International Publication No. WO2012/140022. Despite these efforts, there remains a need in the art forsystems and methods which improve the health assessment and managementof dialysis patients.

SUMMARY OF THE INVENTION

In one embodiment, the present invention provides a system and methodfor monitoring, through Raman spectrometry in real-time and at the pointof patient care, the efficiency of dialysis or transplant allograftfunction in removing toxic metabolic wastes and other target analytesfrom patients with End Stage Renal Disease (ESRD). Embodiments of thesystem and method of the invention capture a ‘molecular footprint’ ofRaman spectra that indicate the presence or concentration of one or moretarget analytes during dialysis sessions and dynamically analyze theirremoval from blood. This also includes similar measurements in urinefrom dialysis or renal transplant patients. Through the use ofalgorithms, the Raman spectra of one or more target analytes may becompared to those stored in memory, and one or more dialysis treatmentoutcome characteristics may be determined. In addition, the system andmethod may be used to determine unique metabolic waste signatures forindividual patients and provide a means for longitudinal assessment ofdialysis efficiency during individual or chronologically separatedialysis treatments, and from these assessments treatment decisions maybe made. Further, effects of intercurrent disease morbidities on theproduction of toxic metabolic wastes can be assessed with the system andmethod of the present invention, further improving individual patientcare. Embodiments of the system of the invention can be economicallyretrofitted to existing hemodialysis systems as well and usedeffectively in multipatient hemodialysis centers. As well, embodimentsof the system of the invention can be applied to the analysis of urineor other fluids from patients being treated for renal failure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate certain aspects of embodiments ofthe present invention, and should not be used to limit the invention.Together with the written description the drawings serve to explaincertain principles of the invention.

FIG. 1 is a schematic diagram of a conventional hemodialysis system.

FIG. 2 is a schematic diagram of an embodiment of a system according tothe invention.

FIG. 3 is a schematic diagram of an embodiment of a method according tothe invention.

FIG. 4 is a flowchart showing an embodiment of a method of theinvention.

FIG. 5 is a table showing characteristics of six hemodialysis patientsand samples associated with them that were the subject of Raman analysisof dialysate.

FIG. 6 is a graph of a Raman spectrum annotated to show characteristicpeaks of representative target analytes.

FIG. 7 is a graph of Raman spectra of the six dialysis patients overthree time points relative to a control sample.

FIGS. 8A-8F are graphs showing the results of Discriminate Analysis foreach patient 1-6, respectively.

FIGS. 9A and 9B are graphs showing the results of Discriminate Analysisfor male and female patients, respectively.

FIGS. 10A and 10B are graphs showing the results of DiscriminateAnalysis for all of the samples for Patients 2-5 and Patients 4-5,respectively.

FIG. 11 is a graph of a Raman spectrum of a Control Sample showing PeakAnalysis.

FIG. 12 a graph of Raman spectra obtained from the six dialysis patientsat three time points relative to control showing Peak Analysis.

FIG. 13 is a graph of a Raman spectra of Patient 4 over three timepoints showing Peak Analysis.

FIGS. 14-20 are graphs showing the kinetics of different target analyteclearances over three time points during the hemodialysis treatment foreach of the six patients, with FIGS. 14A and 14B comparing ureaclearances with that of creatinine, FIG. 15 showing carbohydratemodulation, FIG. 16 showing low MW triglyceride clearances, FIG. 17showing glycine secretion, FIG. 18 showing aspartic acid/asparaginemodulation, FIG. 19 showing glutamic acid/glutamine modulation, and FIG.20 showing sodium bicarbonate secretion.

FIG. 21 is a graph showing that chemometric analysis of Raman spectraindicated that the six patients have different starting/finish pointsand paths.

FIG. 22 is a graph showing chemometric analysis of just the finishingpoints, indicating that the patient's kinetics tend to converge towardthe healthy control, yet all six patients finished treatment with adifferent physiology.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS OF THE INVENTION

Reference will now be made in detail to various exemplary embodiments ofthe invention. It is to be understood that the following discussion ofexemplary embodiments is not intended as a limitation on the invention.Rather, the following discussion is provided to give the reader a moredetailed understanding of certain aspects and features of the invention.

Raman spectroscopy is a spectroscopic technique based on inelasticscattering of monochromatic light (termed the “Raman effect”). Thisoccurs when a photon of monochromatic light interacts with a molecule tobe measured (sample), resulting in absorption and reemission(scattering) at a different frequency than the monochromatic light.Different functional groups produce characteristic peaks in a plot ofintensity versus Raman shift. In this way, the chemical composition of asample may yield a molecular “fingerprint” which can be analyzed. Ramanspectroscopy is particularly advantageous in that it can be used on avariety of samples with little or no sample preparation, and requiressmall sample volumes (e.g. 1 μl).

The present inventors have determined that several Raman spectroscopicmethods can be used to acquire meaningful signatures of chemicalcomposition of analytes in biological fluids and other samples. When theanalytical data from these spectroscopic measurements arecomputationally normalized and then processed and further analyzed, theresultant data produces biologically relevant information that can beused to determine states of health and disease.

Embodiments of the present invention provide methods and systems basedon applied methods of Raman spectroscopy that will allow a clinician toanalyze the molecular components in fluid samples and to utilize theanalysis of these components to assess health and disease, and inparticular, the health of dialysis and transplant patients. Inembodiments, the system provides objective quantitative data that, whencombined with disease-specific logical algorithms, can be used to modifydisease treatments and which can assist in the development of individualpatient-specific medical therapy.

Provided by embodiments of the invention is a method for determiningdialysis treatment efficacy, the method comprising: providing a Ramanspectrum of analytes present in a patient sample associated with adialysis patient; providing one or more Raman spectrum of analytespresent in one or more reference sample associated with the dialysispatient and/or associated with other dialysis patients; comparing theRaman spectrum of the dialysis patient with the one or more referencesamples to determine whether an analyte pattern of two or more selectanalytes is present in both; and based on the comparing, determiningdisease state status of the patient. Similarly, embodiments can be usedto determine functioning of renal transplants by analysis of urinespecimens from transplant patients.

According to methods of the invention, the analyte pattern can comprisetwo or more analytes chosen from creatinine, uric acid, uric acid basedcompounds, interleukin-8, bilirubin, a1-anti-trypsin, arginine,homoarginine, urea, urea-based compounds, urea nitrogen based compounds,ammonium-based compounds, nitrogen-based compounds, vitamins, vitamin C,vitamin B12, folic acid, zinc, amino acids, proteins, nucleic acids,pharmaceutical compounds, 2-heptanal, 2-hexenal, 2-nonenal, 4-decenal,4-HO-decenal, 4-HO-hexenal, 4-HO-nonenal, 4-HO-octenal, 4pyrididone-3-carboxy-1-β-D-ribonucleoside, 8-Hydroxy-2′deoxyguanosine, αKeto δ guanidinovlaeric acid, antranilic acid, argininic acid,asymmetric and symmetric dimethylarginine, cysteine, decanal,dimethylamine, ethylamine, guanidine, guanidinoacetic acid, guanidinesuccinic acid, hepatanal, hexanal, hypoxanthine, malondialdehyde,methylguanidine, monomethylamine, neopterine, nicotinamide, Nmethyl-2-pyridone-5-carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, dimethylarginine,trimethylamine, trimethylamine-N-oxide, 3carboxy-4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-β-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, thiocyanate, α1-acid glycoprotein,α1-microglobulin, β-trace protein, β2 microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, reduced glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, hemoglobin, myoglobin, osteocalcin, parathyroid hormone(PTH), prolactin, resistin, retinal binding protein, solubleintracellular adhesion molecule-1, TNF-α, and vascular endothelialgrowth factor. The number of analytes selected for the fingerprintpattern is not critical and can range from 2 to 1,000, such as from5-500, or from 10-300, or from 15-200, or from 25-100, or any range inbetween. Any one or more of the analytes mentioned in this specificationcan be used, and in any combination, even with other analytes.

Methods of embodiments of the invention can further comprise determiningif there are any differences between the analyte patterns of the Ramanspectrum of the reference and patient samples. Such analysis is helpfulfor determining the extent of a disease state of a patient, especiallyto determine if a patient's health is improving or deteriorating inresponse to hemodialysis treatment.

According to such methods, the one or more reference sample is typicallycollected at a time previous to collection of the patient sample. Thereference sample can also be collected after the treatment, ifassociated with other dialysis or transplant patients that are not thesubject dialysis or transplant patient. In embodiments, the one or morereference sample is associated with the dialysis or transplant patient,and/or associated with other dialysis or transplant patients.

The patient sample of such methods is preferably a dialysate, blood, orplasma, or urine, but can be any sample from or associated with apatient.

In one embodiment, the present invention provides a method formonitoring target analytes in one or more biological fluids of interest.The methods include irradiating the biological fluid of interest withlight to produce one or more spectrum and detecting the spectrum with adetector. The biological fluids of interest are preferably those relatedto dialysis, including hemodialysis and peritoneal dialysis. Forexample, the biological fluids of interest irradiated with light mayinclude blood entering a hemodialysis system, blood exiting ahemodialysis system, used dialysate exiting a dialysis system, or urine.In a preferred embodiment, the fluids are irradiated with monochromaticlight, and one or more Raman spectra are detected as a result of theirradiation. The fluids may be irradiated within the dialysis tubingitself, or removed from the dialysis tubing and irradiated in a separatechamber.

The one or more Raman spectra may represent one or more target analytesthat are indicative of the health of a dialysis or transplant patient.Thus, one embodiment of the invention comprises the steps of a)irradiating a sample representing a biological fluid of interest,wherein the biological fluid of interest relates to a dialysis ortransplant patient b) detecting one or more Raman spectra of one or moretarget analytes from the sample and c) determining the health of thedialysis or transplant patient based on the Raman spectra of the one ormore target analytes. In this embodiment, the health of the dialysis ortransplant patient may include any observation that is associated withdialysis or transplantation of ESRD patients, including an assessment ofuremia, an observation of uremic toxicity, an observation of loss ofvital nutrients, an observation of altered pharmacokinetics of apharmaceutical being administered to a patient, the presence or risk forcomplications associated with dialysis, transplantation, or end-stagerenal failure, and the like. The one or more target analytes may includecombinations of analytes that provide characteristic signatures of theabove.

In another embodiment, the one or more Raman spectra may represent oneor more target analytes that are used to monitor the efficacy of adialysis treatment. Thus, one embodiment of the invention comprises thesteps of a) irradiating a sample representing a biological fluid ofinterest, wherein the biological fluid of interest relates to a dialysispatient, b) detecting one or more Raman spectra of one or more targetanalytes from the sample, and c) monitoring the efficacy of the dialysistreatment based on the Raman spectra of the one or more target analytes.In this embodiment, the efficacy of dialysis treatment may be monitoredby following the change in concentration, or kinetics, of one or moretarget analytes over time. The one or more target analytes may includeurea or other related nitrogenous compounds, or may be distinct from andunrelated to urea. The target analytes may include combinations ofanalytes that provide characteristic signatures on the progress ofdialysis treatment.

In an additional embodiment, the method above may further include: d)making a dialysis treatment decision based on the monitoring of theefficacy of the dialysis treatment. The dialysis treatment decision mayinclude 1) increasing or decreasing the amount of time that the patientis on the dialyzer, 2) increasing or decreasing the blood flow throughthe dialyzer, or 3) a combination of these.

In embodiments of the methods of the invention, either the presence orthe concentration of the one or more target analytes may be monitored.The one or more target analytes may be compared to one or more referenceanalytes. For example, the presence or concentration of one or moretarget analytes may be followed in a single patient and compared to oneor more reference values. The one or more reference values may beindicative of a clinical observation associated with dialysis treatment.For example, in one embodiment, when one or more target analytes exceedconcentrations stored in memory, a finding of uremic toxicity may beinferred.

Additionally, the presence or concentration of one or more targetanalytes in a single patient may be compared to those measured in thefluids of other dialysis or transplant patients or groups of dialysis ortransplant patients. The other patients or groups of patients may havecommon clinical characteristics related to end-stage renal failure,dialysis, or transplantation, including efficacy or complications oftreatment, and these may be indicated by characteristic signatures ofRaman spectra. In this way, Raman spectra from an individual patient maybe compared to those representing a clinical observation associated withdialysis or transplantation treatment, and from this, clinicalobservations may be inferred from the Raman spectra of the patient.

In another embodiment, the present invention provides a method formonitoring dialysis of a patient. The method may comprise a) irradiatinga sample of blood or dialysate from a dialysis patient, b) obtainingRaman spectra of one or more target analytes, c) comparing the obtainedspectrum to one or more Raman spectra stored in memory, and d) analyzingor comparing the obtained Raman spectra with those stored in memory todetermine a dialysis treatment characteristic of a patient. The blood ordialysate can be irradiated with a probe that is configured to emitmonochromatic light and receive Raman scattered light. The probe can beconfigured to irradiate a sample in a dialysis tube of a patient inreal-time or near real-time to determine the presence and/orconcentration of an analyte in the dialysis tube. Alternatively thesample may be separated from the flow of dialysis fluid or may be aurine specimen or other biologically-derived fluid for carrying out thedetermination of the presence and/or concentration of the analytes.

In another embodiment, shown in FIG. 2, the present invention provides asystem comprising a Raman spectrometer 110 operably connected to acomputing device 120, through a cable 105 (or wireless connection, notshown). The computing device 120 comprises an input/output device 122and one or more processors 124 operably connected to a memory 130. Thememory can comprise a set of computer-executable instructions 132 forperforming the algorithms of the invention and one or more databases134, 136 for storing spectra obtained from the Raman spectrometer. Thedatabases 134, 136 may include patient-specific, group-specific, and/ordialysis treatment outcome-specific Raman spectral signatures. Inembodiments, the Raman spectrometer 110 may comprise an excitationsource, a sampling apparatus, and a detector. In one embodiment, theexcitation source is a laser, the detector is a spectrometer, and thesampling apparatus is a fiber optic probe 140 that terminates in chamber142 connected to the dialysate waste output tube 155 coming fromdialyzer 170, which tube ends in waste dialysate collection container158. Dialyzer 170 has fresh dialysate intake 165 originating from freshdialysate container 162. Dialyzer 170 also has removed blood input 172originating from a patient (not shown) and clean blood output 177returning to the patient.

The Raman spectrometer may be a benchtop or a handheld spectrometer.Examples of benchtop Raman spectrometers include those described in U.S.Pat. Nos. 5,786,893; 5,534,997; and U.S. Pat. No. 6,100,975. Examples ofhandheld Raman spectrometers are described in U.S. Pat. No. 7,505,128;U.S. Pat. No. 7,524,671; U.S. Pat. Nos. 7,651,851 and 8,699,020, andU.S. Patent Application Publication No. 20140052386 A1. The fiber opticprobe may have a dichroic mirror, which separates Raman scattered lightfrom laser light by reflecting laser light and allowing Raman-scatteredwavelengths to pass. Laser light and Raman scattered light may betransmitted through separate fibers (collection fiber(s) and excitationfiber(s)). Filters may be placed before the fibers for blockingundesirable wavelengths, such as a long pass filter placed before thecollection fiber (blocks reflected laser light) and a band-pass filterplaced before the excitation fiber (blocks Raman scattered light). Thefiber optic probe may include one or more lenses for focusing the lightonto the sample or onto the fibers. An example of such a fiber opticprobe is the RAMANPROBE™, described in U.S. Pat. No. 5,112,127. Anotherexample is a Raman fiber optic probe embedded in a microfluidic device,described in U.S. Pat. No. 8,638,431. Another example is a dual andmulti-wavelength Raman sampling probe described in U.S. PatentApplication Publication No. 20120099102.

The laser may emit monochromatic light at any wavelength, including farinfrared, mid infrared, infrared, near infrared, visible light,ultra-violet, and extreme-ultraviolet, or at multiple wavelengths. Thechoice of wavelength may depend on the target molecule one wishes tomeasure. For example, for visible wavelengths such as blue or green canbe good for inorganic molecules, while ultraviolet wavelengths may beoptimal for measuring biomolecules such as proteins, RNA, and DNA asthese tend to absorb UV radiation. In addition, embodiments may includemultiple lasers to represent multiple wavelengths.

The Raman spectra stored in memory may be those from the same patient,and/or those from different patients. In one embodiment, the Ramanspectra stored in memory are obtained from one or more earlier samplesduring the course of dialysis. By comparing the Raman spectra from acurrent sample to that of one or more previous samples, the time courseof dialysis may be monitored. Through this way, the kinetics of one ormore target analytes may be followed, either in the blood or the wastedialysate of the patient, and the progress of the dialysis may bemonitored as well as potential complications. The target analytes mayinclude one or more uremic toxins, nutritional factors, electrolytes,and pharmaceutical compounds or metabolites. The target analytes mayinclude urea or related compounds, or may be unrelated to urea.

In another embodiment, the Raman spectra stored in memory may be thosefrom different patients. For example, the Raman spectra stored in memorymay be from one or more groups of patients, wherein each group sharesone or more dialysis treatment characteristics. In this embodiment,Raman spectra obtained from the patient can be compared with thoserepresentative or not representative of a dialysis treatmentcharacteristic using an algorithm which classifies the patient's spectraaccording to how much it resembles the spectra of the two groups. Inthis way, the patient may be assigned a dialysis treatmentcharacteristic based on the classification. The dialysis treatmentcharacteristic may include successful treatment, failed treatment,incomplete treatment, or complicated treatment.

In embodiments, the Raman spectra provide characteristic signatures thatare indicative of one or more dialysis treatment characteristics. TheRaman spectra may represent a single analyte or multiple targetanalytes, including anywhere from 2 to 100 analytes or more. Thecharacteristic signatures may indicate a variety of treatment outcomes.For example, one group of Raman spectra may be highly indicative ofuremic toxicity, while another group of Raman spectra may be highlyindicative of a nutritional deficiency resulting from the dialysisprocedure. Another group of Raman spectra may be indicative of alteredpharmacokinetics of a pharmaceutical resulting from the dialysisprocedure. Another group of Raman spectra may indicate complications ofeither the dialysis procedure itself or end-stage renal disease. Thegroups of Raman spectra may be generally composed of the spectra of 2-10analytes, but larger groups are possible.

Thus, one embodiment of the invention provides a method comprising a)providing a plurality of Raman spectra representative of one or moretarget analytes stored in memory, b) receiving spectra from a Ramanspectrometer of the one or more target analytes obtained from a samplefrom a dialysis patient, and c) analyzing the received spectra with aprocessor; wherein the analysis is based on the plurality of spectrastored in memory and provides an indication of a dialysis treatmentcharacteristic of the patient.

In one aspect of this embodiment the plurality of Raman spectra areobtained from samples from at least a first and second group of patientshaving different dialysis treatment characteristics and an algorithm isused to classify the dialysis treatment characteristic of the patientbased on the plurality of Raman spectra of the first or the second groupof patients.

In another aspect of this embodiment, the plurality of Raman spectrarepresentative of one or more target analytes stored in memory areobtained from a sample of the patient at a first time point, the spectrais received at second later time point, and the spectra from the secondtime point is compared to the spectra of the first time point to providean indication of the dialysis treatment characteristic of the patient

Another embodiment comprises a method for monitoring the health ofdialysis patients, the method comprising a) receiving spectra from aRaman spectrometer of one or more target analytes obtained from a firstsample from a dialysis patient at a first time point, b) storing thespectra in memory, c) receiving spectra from a Raman spectrometer of oneor more target analytes obtained from a second sample from the dialysispatient at a second later time point, d) comparing the Raman spectrafrom the second time point with the Raman spectra from the first timepoint with a processor, and e) determining a dialysis treatment outcomecharacteristic of the dialysis patient based on the comparison.

The one or more target analytes can be urea or related molecules. Inother embodiments, the target analytes are not related to urea. The oneor more target analytes may be selected from the group comprising,consisting of, or consisting essentially of uremic toxins, nutritionalfactors, electrolyes, and pharmaceutical compounds (such asacetaminophen or acetylsalicylic acid) or metabolites.

The dialysis treatment characteristic may be selected from the groupcomprising, consisting of, or consisting essentially of successfultreatment, failed treatment, and complicated treatment. Further, thedialysis treatment outcome characteristic may be based on the kineticsof the one or more target analytes. In embodiments, the method mayfurther comprise making a treatment decision based on the dialysistreatment characteristic. For example, the treatment decision maydetermine whether to continue or terminate dialysis treatment.

Another embodiment comprises a method for monitoring the health ofdialysis patients, the method comprising a) providing one or morereference sets of a plurality of Raman spectra of one or more targetanalytes stored in memory, wherein the reference sets represent one ormore dialysis treatment outcome characteristics, b) receiving spectrafrom a Raman spectrometer of the one or more target analytes obtainedfrom a sample from a dialysis patient, c) analyzing the received Ramanspectra with a processor according to an algorithm, wherein the analysisis based on a comparison of the received Raman spectra with theplurality of Raman spectra stored in memory, and d) determining adialysis treatment outcome characteristic of the dialysis patient basedon the analysis.

Another embodiment provides a method comprising a) providing a pluralityof Raman spectra representative of one or more target analytes stored inmemory wherein the plurality of Raman spectra are obtained from samplesfrom at least a first and second group of dialysis patients, wherein thefirst group of patients share a first dialysis treatment outcomecharacteristic and the second group of patients share a second dialysistreatment outcome characteristic, b) receiving spectra from a Ramanspectrometer of the one or more target analytes obtained from a samplefrom a dialysis patient, c) analyzing the received spectra with aprocessor according to an algorithm, wherein the analysis is based onthe plurality of spectra stored in memory and is used to classify thepatient's treatment outcome characteristic according to the first or thesecond group of patients.

In one aspect of these embodiments, the first dialysis treatment outcomecharacteristic is the presence of uremia and the second treatmentoutcome characteristic is the absence of uremia.

In another aspect of these embodiments, the first dialysis treatmentoutcome characteristic is the presence of one or more uremic toxinsabove a concentration threshold and the second dialysis treatmentoutcome characteristic is the absence of one or more uremic toxins abovethe concentration threshold.

In another aspect of these embodiments, the first dialysis treatmentoutcome characteristic is the presence of one or more nutritionalfactors above a concentration threshold and the second dialysistreatment outcome characteristic is the absence of one or morenutritional factors above the concentration threshold.

In another aspect of these embodiments, the first dialysis treatmentoutcome characteristic is the presence of one or more complications fromend-stage renal failure and the second dialysis treatment outcomecharacteristic is the absence of one or more complications of end-stagerenal failure.

In another aspect of these embodiments, the first dialysis treatmentoutcome characteristic is the presence of one or more dialysiscomplications and the second dialysis treatment outcome characteristicis the absence of one or more dialysis complications.

In another aspect of these embodiments, the first dialysis treatmentoutcome characteristic is successful treatment and the second dialysistreatment outcome characteristic is treatment failure.

In the methods of the invention, the analytes (or “target analytes” usedinterchangeably herein) can be measured in various fluid lines of akidney hemodialysis machine or from a peritoneal catheter in the case ofperitoneal dialysis. Kidney dialysis machines are well known in the artand are illustrated, for example, in U.S. Pat. Nos. 3,598,727,4,172,033, 4,267,040, and 4,769,134 6,284,131. In one embodiment, theanalytes are measured in the used dialysate (dialysate exiting thedialyzer). In another embodiment, the analytes are measured in bloodentering the hemodialysis machine (from the patient). In anotherembodiment, the analytes are measured in the blood exiting thehemodialysis machine (to the patient). Used dialysate will typicallycontain target analytes (e.g. waste products) extracted from bloodentering the dialyzer. The other constituents of used dialysate areexcess electrolytes leaving the dialyzer. However, used dialysate shouldprovide a cleaner sample as it is absent Raman spectra from bloodconstituents. As used herein a “sample from a dialysis patient” includesdirect samples (e.g. blood leaving the dialysis patient) as well asindirect samples (e.g. blood exiting the dialysis machine or wastedialysate).

The analytes can be measured in the tubing entering or exiting thehemodialysis machine. For example, in one embodiment, a laser beam canbe directed into a segment of the dialysis tubing (used in eitherhemodialysis or peritoneal dialysis). In another embodiment, a probesuch as a miniaturized fiber optic probe can be inserted into thedialysis tubing. In another embodiment, surface enhanced Ramanspectroscopy (SERS) sensors, such as those described in U.S. Pat. No.8,953,159, are placed in the dialysis tubing. Alternatively or inaddition, one or more of the analytes can be measured in samples takenfrom the patient, including blood, plasma, urine, feces, saliva, andcerebrospinal fluid.

In one embodiment, the analytes are intermediates or end-products ofmetabolic processes occurring in the patient. The intermediates orend-products may be a result of anabolism, catabolism, energytransformations, xenobiotic transformations, microbial reactions, amongothers. The intermediates or end-products may be freely soluble inblood, or bound to proteins specifically or non-specifically, and may beorganic or inorganic molecules, and/or found in urine. In anotherembodiment, the analytes are biomolecules such as proteins or nucleicacids. Examples of protein analytes include antibodies or antibodyfragments, structural proteins, enzymes, messenger proteins, andtransport/storage proteins. Non-limiting examples of nucleic acidsanalytes include genomic DNA, mRNA, rRNA, tRNA, siRNA, and ribozymes.Additionally, the analytes may be indicators of the nutritional statusof the patient (e.g. vitamins, minerals, cofactors). In an exemplaryembodiment, the analytes, as described herein, are uremic toxins, orsuspected uremic toxins. The uremic toxins, or suspected uremic toxins,may have (or may be suspected to have) deleterious effects on a dialysispatient as a result of a shift in blood concentration in comparison tolevels that occur in the population with normal renal function. Theuremic toxins may have deleterious effects through a variety ofmechanisms, such as by inhibiting enzymes, transport functions, proteinsynthesis, DNA synthesis, energy transformations, metabolism, and thelike. Alternatively or in addition, one or more of the analytes mayinclude one or more xenobiotics (e.g. pharmaceutical compounds).

In embodiments, analytes that can be measured according to the methodsof the invention include, but are not limited to, free water-soluble lowmolecular weight molecules, protein-bound molecules, and mid-rangemolecular weight molecules. Examples of free water-soluble low molecularweight molecules that can be measured according to methods of theinvention include, without limitation 2-heptanal, 2-hexenal, 2-nonenal,4-decenal, 4-HO-decenal, 4-HO-hexenal, 4-HO-nonenal, 4-HO-octenal,4-pyrididone-3-carboxy-1-β-D-ribonucleoside, 8-Hydroxy-2′deoxyguanosine, α-Keto-δ-guanidinovlaeric acid, antranilic acid,argininic acid, asymmetric dimethylarginine, cysteine, decanal,dimethylamine, ethylamine, guanidine, guanidinoacetic acid, guanidinesuccinic acid, hepatanal, hexanal, hypoxanthine, malondialdehyde,methylguanidine, monomethylamine, neopterine, nicotinamide,N-methyl-2-pyridone-5 -carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, symmetricdimethylarginine, trimethylamine, trimethylamine-N-oxide, and uric acid.Examples of protein-bound molecules that can be measured according tomethods of the invention include, without limitation3-carboxy-4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-β-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, and thiocyanate. Examples of mid-rangemolecular weight molecules that can be measured according to methods ofthe invention include, without limitation, α1-acid glycoprotein,α1-microglobulin, β-trace protein, β2-microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, oxidized glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, hemoglobin, myoglobin, osteocalcin, PTH, prolactin,resistin, retinal binding protein, soluble intracellular adhesionmolecule-1, TNF-α, and vascular endothelial growth factor. Additionally,the analytes may include any molecule listed in the EUTox Uremic ToxinDatabase (maintained by the EUTox Work Group, a working group of the“European Society of Artificial Organs” (ESAO) and an endorsed workinggroup of the “European Renal Association-European DialysisTransplantation Association” (ERA-EDTA)). The analytes may include anymolecule recited above, or any combination of molecules recited above.

In some cases, the target analytes may indicate pathological processesin a dialysis patient, such as a necrotic process (e.g. liver enzymes)or an inflammatory process (cytokines). Additionally, the targetanalytes may be factors that are prognostic or diagnostic of otherpathological processes (cancer, heart disease). By measuring thesetarget analytes in the blood, urine, or dialysate of a patient, thehealth of the patient may be inferred.

The presence and/or the concentration of the analytes may be determined.The analytes may be measured either as raw spectral intensitymeasurements or normalized measurements, or converted to concentrationvalues. For example, U.S. Pat. No. 7,326,576 discloses the ability ofRaman spectroscopy to produce a concentration correlation with intensityvalues for urea measured in human blood plasma samples to whichincreasing amounts of urea were added. The results showed a linearcorrelation between the peak height at 1001 cm⁻¹ and urea concentration.In embodiments of this invention, however, this invention does not limitits analysis to urea, and includes other target analytes includingcombinations or groups of analytes which may be more clinicallysignificant measures of uremic toxicity than urea itself. As providedabove, urea itself is relatively non-toxic and does not contributesignificantly to the symptoms of uremia. However, it is conceivable thatthe concentrations of other analytes could be determined in a similarmanner, not just limited to blood but other medical fluids of interestsuch as dialysate, urine, feces, etc. In embodiments, Raman-basedCHEMOMETRIC FINGERPRINTING™ can be performed, where a fingerprint orpattern of a number of select analytes is compared between the testsample (patient sample) and the reference data to determine health of apatient and/or efficacy of treatment.

In one embodiment, the analytes are selected to provide a measurement oftoxicity to the patient. In one embodiment, the analytes includeurea-based compounds, urea nitrogen based compounds, ammonium-basedcompounds, uric acid based compounds, and nitrogen-based compounds.Hence, in one embodiment, the methods of present invention are directedto monitoring the levels of blood urea nitrogen in patients undergoingdialysis. However, in other embodiments, the analytes include moleculesor a combination of molecules that don't reflect the level of blood ureanitrogen, but provide a better indication of uremic toxicity. Thus, insome embodiments, the analyte or analytes are not urea or not chemicallyrelated to urea.

The term “level” or “levels” of an analyte, as used herein, refers toconcentration of a constituent, such as a constituent of a dialysatefluid. The concentration levels are readily derivable from Ramanspectral measurements. Thus, the “level” can also be measured based onRaman spectral data. Without the need, in all instances, to convert suchdata into concentration values. The term “level” is also meant toinclude the magnitude of a quantity considered in relation to anarbitrary reference value. For example, a look-up table can be used forplotting predictions of analyte concentration versus a referencecomprising of healthy individuals or threshold concentrations in healthyindividuals. In other embodiments, a standard curve generated from knownanalyte concentrations can be used.

In carrying out embodiments of methods of the invention, severalcommercially-available Raman spectroscopic devices can be used forinitial data acquisition from samples, and a number of other devicesthat are commercially-available can be used to increase the convenienceand accuracy of the Raman spectroscopic analytical data. These devicesare manufactured by various commercial ventures and are readilyavailable.

In embodiments of the method, fluids generated during human patienthemodialysis or peritoneal dialysis, specifically the dialysate wastestream, contain dialyzable molecules that can be collected during thedialysis process and analyzed with Raman spectroscopic methods. Patienturine specimens may also be collected and analyzed by these methods.Once raw data acquisition has taken place, the raw spectroscopicinformation can be subjected to computational normalization and then tocomplex computational analysis through one or more algorithms. Thealgorithms may be any one of machine learning or predictive orclassification algorithms such as hierarchical clustering, k-meansclustering, linear discriminant analysis, principle components analysis,logistic regression, support vector machines, k-nearest neighbor,decision trees, neural networks, Bayesian networks, and Hidden Markovmodels.

The analysis of specific molecules in the waste dialysate, urine, orother fluids, derived from computations of the invention can be used todetermine observations relevant to the management of kidney failure withdialysis or after transplantation. This includes evaluation of thehealth of dialysis patients, evaluation of the efficacy of the dialysistreatment, and longitudinal health monitoring. These observations mayrelate to diagnostic or prognostic biomarkers of complications of endstage renal disease or complications of the dialysis treatment itself.The diagnostic or prognostic biomarkers may be population-specific,race-specific, age-specific, gender-specific, or patient-specific. Thediagnostic or prognostic biomarkers may indicate or predict one or morecomplications of end-stage renal disease, including anemia, bleedingfrom the stomach or intestines, bone, joint, and muscle pain, changes inblood sugar (glucose), nerve damage, fluid buildup around the lungs,high blood pressure, heart attack, and heart failure, high potassiumlevels, increased risk of infection, liver damage or failure,malnutrition, miscarriages or infertility, restless legs syndrome,stroke, dialysis dysequilibrium syndrome, seizures, and dementia,swelling and edema, and weakening of the bones and fractures.Conversely, the analytes may be prognostic of favorable treatmentoutcomes of dialysis where these complications are prevented or delayed.

The system and method of the invention are useful for a variety ofapplications related to ongoing monitoring and the management of healthof dialysis and renal transplant patients. In one application, the ureareduction efficiency for a patient undergoing dialysis can be determinedin real-time or near real-time and a kinetic plot of dialysis efficiencycan be generated based on the urea reduction rate kinetics. Efficiencymay be determined by one of two parameters. Urea reduction ratio (URR)indicates the reduction in urea as a result of dialysis and is commonlyexpressed as a percentage. Kt/V is another indicator of dialysisadequacy, where K stands for the dialyzer clearance, the rate at whichblood passes through the dialyzer, expressed in milliliters per minute(mL/min), t stands for time, Kt, is clearance multiplied by time,representing the volume of fluid completely cleared of urea during asingle treatment, and V is the volume of water a patient's bodycontains. The Kt/V is mathematically related to the URR and is in factderived from it, except that the Kt/V also takes into account twoadditional factors 1) urea generated by the body during dialysis 2)extra urea removed during dialysis along with excess fluid. A patient'sURR or Kt/V can be increased either by increasing time on dialysis orincreasing blood flow through the dialyzer.

In another application, the chemical composition of the dialysate wastestream can be determined in real-time or near real-time, which can beused to determine the clearance of many medically important moleculesduring the process of dialysis. The medically important molecules may beany of the analytes described above. The analytes may be used asindicators of dialysis efficiency or as indicators or predictors ofcomplications of failed dialysis or end-stage renal failure or asindicators or predictors of successful dialysis treatment.

In another application, the presence and amount of certain moleculesthat are by-products of metabolism, termed “uremic toxins” or “suspecteduremic toxins” as discussed previously, can be determined in real-timeor near real-time, and these can be partially or fully acquired throughdialysis and which may be present in the dialysate waste stream or thepatient's blood. These may be used as indicators or predictors ofcomplications of inadequate dialysis treatment.

In another application, the efficiency and accuracy of each individualdialysis treatment for each patient can be determined, and thisinformation can be stored, compared, and used on an ongoing temporalbasis to determine adequacy or limitations of therapy. These storedmeasurements can be subsequently used to revise and plan dialysistherapy specific for each patient and which could be adjusted to changesin the health and well-being of the patient.

In another application, comparison of samples from individuals, betweenindividuals, and among groups of individuals undergoing dialysistreatment, the presence or persistence of novel molecules that mayindicate changes in the patterns of disease, including insights intoetiologies, comorbidities, and progression to treatment failure, can beascertained.

In another application, by application of these tools and methods,advantages and disadvantages associated with specific components ofhemodialysis machines, including dialyzer coils and dialyzer coilmembranes, can be gauged.

In another application, by application of these tools and methods,advantages and disadvantages associated with the frequency and type ofhemodialysis can be obtained, including benefits and risks of the use ofhigh- and low-flux dialyzer membranes, and more/less frequent use ofdialysis (number of treatments per week, duration of individualtreatments, intervals between treatments),

In another application, the need for nutritional and metabolicsupplementation for individual patients to replace or maintainbiologically-important molecules such as vitamins, minerals, and aminoacids, that may be unintentionally lost during the process of dialysis,may be ascertained. This can be based on the presence of these nutrientsin fluids such as dialysate, blood, and urine. From these values, thedegree and kinetics of loss during the process of dialysis can beobtained.

In another application, modifications that can be made to thecomposition of the dialysate fluid can be determined, making it moreefficient for the dialysis process and improving the temporal healthoutcomes for the patient. The modifications can be made based on theefficiency of the dialysis determined from methods of the invention.

In another application, the need for changing (increasing/decreasing)the dosages of pharmaceuticals used to manage patient disease that maybe unintentionally lost during the process of dialysis can bedetermined. This can be based on the presence of the parent compound ormetabolites in fluids such as dialysate, blood, and urine. From thesevalues, the degree and kinetics of loss during the process of dialysiscan be obtained.

In another application, molecules that are markers of disease that maybe then used as the basis for improved medical management withpharmaceuticals can be ascertained.

In another application, the viability of renal transplants can beevaluated. Blood and urine samples can be measured to determine renalclearances of urea or alternative markers of renal sufficiency.

In another application, the system and method of the invention can beused for drug metabolism/kinetics studies. These studies can beperformed on end-stage renal failure patients (including those receivingand not receiving dialysis) as well as patients with normal renalfunction.

A method of determining a patient-specific dialysis prescription is alsoincluded in embodiments of the invention, the method comprising:subjecting a patient to a dialysis treatment based on a first dialysisprescription; during the dialysis treatment, taking multiple samplesassociated with the patient; measuring a Raman spectrum of analytespresent in the samples; determining the kinetics of the analytes basedon the Raman spectrum; and modifying the first dialysis prescriptionbased on the kinetics of the analytes to produce a second dialysisprescription that is patient-specific. According to such methods thesamples can be taken at various time interval, such as at the beginning,middle, and end of the dialysis treatment.

The analytes monitored according to such methods can be chosen from oneor more of creatinine, uric acid, uric acid based compounds,interleukin-8, bilirubin, a1-anti-trypsin, arginine, homoarginine, urea,urea-based compounds, urea nitrogen based compounds, ammonium-basedcompounds, nitrogen-based compounds, vitamins, vitamin C, vitamin B12,folic acid, zinc, amino acids, proteins, nucleic acids, pharmaceuticalcompounds, 2-heptanal, 2-hexenal, 2-nonenal, 4-decenal, 4-HO-decenal,4-HO-hexenal, 4-HO-nonenal, 4-HO-octenal, 4pyrididone-3-carboxy-1-β-D-ribonucleoside, 8-Hydroxy-2′deoxyguanosine, αKeto δ guanidinovlaeric acid, antranilic acid, argininic acid,dimethylarginine, cysteine, decanal, dimethylamine, ethylamine,guanidine, guanidinoacetic acid, guanidine succinic acid, hepatanal,hexanal, hypoxanthine, malondialdehyde, methylguanidine,monomethylamine, neopterine, nicotinamide, Nmethyl-2-pyridone-5-carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, dimethylarginine,trimethylamine, rimethylamine-N-oxide, 3carboxy4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-β-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, thiocyanate, α1-acid glycoprotein,α1-microglobulin, β-trace protein, β2 microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, reduced glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, myoglobin, osteocalcin, parathyroid hormone (PTH),prolactin, resistin, retinal binding protein, soluble intracellularadhesion molecule-1, TNF-α, and vascular endothelial growth factor.

Samples associated with the patient can be a dialysate, or any bodyfluid taken from the patient, such as blood samples, plasma samples,and/or urine samples.

Based on the results of such analysis, modifying of the first dialysisprescription can involve a change in the prescription relative to thefirst prescription where longer or shorter dialysis treatment times areprescribed, an increase or decrease in the rate of blood flow through adialyzer is prescribed, and/or an increase or decrease in the frequencyof dialysis treatments is prescribed for the patient.

Further, in embodiments of the system, probes can be inserted into thedialysate waste stream of hemodialysis equipment that can acquire a“Raman signal” or fluid sample that can be used for acquisition of“Raman signal” for the purposes described above. Additionally, complexdiagnostic algorithms can be used in conjunction with the Raman data toassess the disease state of individuals suffering acute and chronicorgan failure, including kidney failure.

Additional embodiments include automated data collection and analysissystems for processing information from one, two, or more hemodialysismachines simultaneously and providing logical relevant information tohemodialysis personnel for patient management.

Additional embodiments include computational systems and algorithms torelate the levels and interrelationships of various molecules identifiedby sampling to individual patient health.

In additional embodiments, other samples can be analyzed according tothe method of the invention, including, plasma, urine, perfusion fluids,and feces with similar applications and outcomes, with the exceptionthat information would be would be derived from individual patientsamples and not analyzed in real-time.

In addition, other uses for these analytical and computational methodsdescribed/proposed here that fall within the scope of the invention.

FIG. 3 is a flowchart that depicts another embodiment of a method 1000of the invention. As shown, a patient with end-stage renal failurereceives a dialysis prescription 1050 and undergoes hemodialysis 1100.The dialysate waste stream is monitored, and Raman spectrometry data iscontinuously collected 1150. The Raman data undergoes signalconditioning during which the data is filtered and the Raman spectranormalized 1200. A principle components analysis error detection methodis optionally used to purge bad data 1250 so that only the highestquality data is retained. A linear discriminant analysis is optionallyused on the retained data 1300 to monitor the hemodialysis treatmentprogress. The linear discriminant analysis 1300 evaluates treatmentprogress based on a comparison between a vast database of extreme andnormal Raman spectra 1450 which provide a collection of all possiblescenarios and outcomes, along with a patient-specific database andtime-course 1400. As a result, a decision is made based on treatmentprogress and results in an updated treatment protocol 1350, which feedsback to the patient-specific time course 1400. An expert clinician 1500provides expert input and validation to accept, reject, or alter theupdated treatment protocol. At this time, it is determined whethertreatment is complete 1550—if yes, the hemodialysis is finished, and ifno, the treatment protocol is updated, and then fed back into thehemodialysis prescription 1050.

Similar to FIG. 3, FIG. 4 is a flow chart showing an embodiment of amethod of the invention. First, real-time measurements of Raman spectraof target analytes are obtained, including target analytes such as oneor more of urea, carbohydrates, triglycerides, amino acids, smallmolecule markers, and/or uremic toxins. The measurements includeabsolute values and dynamics. The spectra are fed into a statisticalmodel, which can include CHEMOMETRIC FINGERPRINTING™ analysis, andalgorithms such as artificial neural networks and principal componentsanalysis. Values from a patient database of short and long-term healthresponses of multiple patients may be incorporated into the statisticalmodel. As a result of the statistical model, critical health concernsmay be identified or a custom dialysis prescription or a dynamicdialysis prescription based on patient response may be generated.

It will be understood that some or all of the method steps described inthis specification may be carried out by a group of computer-executableinstructions that may be organized into routines, subroutines,procedures, objects, methods, functions, or any other organization ofcomputer-executable instructions that is known or becomes known to askilled artisan in light of this disclosure, where thecomputer-executable instructions are configured to direct a computer orother data processing device such as a processor to perform one or moreof the specified processes and operations. The computer-executableinstructions may be written in any suitable programming language orlanguages, including C, C++, C#, Visual Basic, Java, Python, Perl, PHP,Html, CSS, and JavaScript.

In other embodiments of the invention, files comprising the set ofcomputer-executable instructions may be stored in computer-readablememory on a single computer or distributed across multiple computers. Askilled artisan will further appreciate, in light of this disclosure,how the invention can be implemented, in addition to software, usinghardware or firmware. As such, as used herein, the operations of theinvention can be implemented in a system comprising any combination ofsoftware, hardware, or firmware.

Embodiments of the invention also include a non-transitory computerreadable medium comprising one or more computer files comprising a setof computer-executable instructions for performing one or more of thecalculations, steps, processes and operations described and/or depictedherein. In exemplary embodiments, the files may be stored contiguouslyor non-contiguously on the computer-readable medium. Embodiments mayinclude a computer program product comprising the computer files, eitherin the form of the computer-readable medium comprising the computerfiles and, optionally, made available to a consumer through packaging,or alternatively made available to a consumer through electronicdistribution. As used in the context of this specification, a“computer-readable medium” includes any kind of computer memory such asfloppy disks, conventional hard disks, CD-ROM, Flash ROM, non-volatileROM, electrically erasable programmable read-only memory (EEPROM), andRAM. In exemplary embodiments, the computer readable medium has a set ofinstructions stored thereon which, when executed by a processor, causethe processor to perform the steps described in this specification. Theprocessor may implement this process through any of the proceduresdiscussed in this disclosure or through any equivalent procedure.

For example, one embodiment of the invention includes one or morenon-transitory computer-readable media, at least one having one or morereference sets of a plurality of Raman spectra of one or more targetanalytes stored thereon, wherein the reference sets represent one ormore dialysis treatment outcome characteristics, and at least one havinga set of computer-executable instructions stored thereon that direct acomputer to a) receive spectra from a Raman spectrometer of the one ormore target analytes obtained from a sample from a dialysis patient, b)analyze the received Raman spectra with a processor according to analgorithm, wherein the analysis is based on a comparison of the receivedRaman spectra with the plurality of Raman spectra stored on thenon-transitory computer-readable media, and c) determine a dialysistreatment outcome characteristic of the dialysis patient based on theanalysis.

Another embodiment of the invention includes a non-transitorycomputer-readable medium having a set of computer-executableinstructions stored thereon that direct a computer to a) receive spectrafrom a Raman spectrometer of one or more target analytes obtained from afirst sample from a dialysis patient at a first time point, b) store thereceived spectra in memory, c) receive spectra from a Raman spectrometerof one or more target analytes obtained from a second sample from thedialysis patient at a second later time point, d) compare the spectrafrom the second time point with the spectra from the first time pointwith a processor, and e) determine a dialysis treatment outcomecharacteristic of the dialysis patient based on the comparison.

In other embodiments of the invention, files comprising the set ofcomputer-executable instructions may be stored in computer-readablememory on a single computer or distributed across multiple computers. Askilled artisan will further appreciate, in light of this disclosure,how the invention can be implemented, in addition to software, usinghardware or firmware. As such, as used herein, the operations of theinvention can be implemented in a system comprising any combination ofsoftware, hardware, or firmware

Embodiments of the invention include one or more computers or devicesloaded with a set of the computer-executable instructions describedherein. The computers or devices may be a general purpose computer, aspecial-purpose computer, or other programmable data processingapparatus to produce a particular machine, such that the one or morecomputers or devices are instructed and configured to carry out thecalculations, processes, steps, and operations of the invention. Thecomputer or device performing the specified calculations, processes,steps, and operations may comprise at least one processing element suchas a central processing unit (i.e. processor) and a form ofcomputer-readable memory which may include random-access memory (RAM) orread-only memory (ROM). The computer-executable instructions can beembedded in computer hardware or stored in the computer-readable memorysuch that the computer or device may be directed to perform one or moreof the processes and operations depicted and/or described herein. Thecomputers or devices may include one or more of the databases describedherein stored in computer memory. The computers or devices may beconnected to a Raman spectrometer described herein through a wired orwireless connection.

Additional embodiments of the invention comprise a computer system forcarrying out the methods of the invention. The computer system maycomprise a processor for executing the computer-executable instructions,one or more databases described herein, a user interface, and a set ofinstructions (e.g. software) for carrying out the method. The computersystem can be a stand-alone computer, such as a desktop computer, aportable computer, such as a tablet, laptop, PDA, or smartphone, or aset of computers connected through a network including a client-serverconfiguration and one or more database servers. The network may use anysuitable network protocol, including IP, UDP, or ICMP, and may be anysuitable wired or wireless network including any local area network,wide area network, Internet network, telecommunications network, Wi-Fienabled network, or Bluetooth enabled network.

Embodiments of the invention include a system comprising a Ramanspectrometer comprising a laser, a spectrometer, and a fiber opticprobe, and a computer comprising an input/output device, a processor,and a memory operably connected to the Raman spectrometer. In thisembodiment, the fiber optic probe is adapted to continuously measure adialysate waste stream from a hemodialysis machine. In one embodiment,to continuously measure the dialysate involves taking measurements atselected time intervals, and preferably automatically or pre-programmedto take measurements at selected times. The memory has one or morereference sets of a plurality of Raman spectra of one or more targetanalytes stored thereon, wherein the reference sets represent one ormore dialysis treatment outcome characteristics. In this embodiment, thelaser is configured to excite one or more target analytes in thedialysate waste stream which correspond to the one or more targetanalytes of the reference sets at a wavelength that produces Ramanspectra.

EXAMPLE

Six patients (three male and three female) with a differing history ofhemodialysis (first instance to 6 years) were the subject of a study formeasuring the kinetics of target analytes during a hemodialysistreatment using Raman spectroscopy. Dialysate samples were collected atbeginning (A), middle (B), and end (C) of the prescription cycle(Samples A, B, and C). All patients were dialyzed against G2231dialysate and Optiflux High Fluid Coil. There was no significantvariation in total prescription time (221-244 minutes). No significantvariation in eating was observed. One patient (Patient #1) was a knowndiabetic. Characteristics of the dialysis patients are shown in thetable in FIG. 5.

For analysis, 500 μL of a sample associated with a patient was placed ona foil tray, and allowed to air dry. This dried sample was analyzedusing the Bruker Senterra Raman microscope. Comparisons were madebetween “dried” and “liquid” phase measurements. Correlations werealready established between Raman bands and experimentally measuredamino acids (UPLC) and fatty acids (GC-MS). Standards were used for ureaand bicarbonate. Literature data are available for carbohydrates andsmall molecules. The Raman Spectra obtained from the OPUS software wasprocessed in Matlab software to normalize the data, and average thespectra of each sample. These were then plotted in Excel. Results of theexperiment fall into two categories, Discriminate Analysis and PeakAnalysis.

Peak Analysis was conducted to identify specific peaks of interest,which correlate to specific chemical compounds that dialysis should beaffecting. Exemplary peaks of interest include Urea (˜1000 cm⁻¹), SodiumBicarbonate (˜1040 cm⁻¹), Glucose/Fatty Acids/Triglycerides (˜1070cm⁻¹), and Carbohydrates (˜930 cm⁻¹).

FIG. 6 is an example of a Raman spectrum obtained in this Example. Fortyfive peaks were identified that change with time and the patient.

FIG. 7 shows Raman spectra of the six dialysis patients over the threetime points relative to a control sample. These spectra were subject toDiscriminate Analysis. The results of Discriminate Analysis for eachpatient are shown in FIGS. 8A-8F. Due to the limited time points, aswell as limited knowledge of the time interval between samples, it isdifficult to determine a mathematical relationship between the Patientsamples. However, the relative diffusivity of the clusters may allow forfurther analysis. FIGS. 9A and 9B show the results of analysis bygender, which did reveal differences between the distinctiveness of theclusters. This could signify differences in effectiveness of thetreatment determined by gender. FIGS. 10A and 10B show a DiscriminateAnalysis of all the samples, which did show difference from the control,as well as from one another. This was true based on history ofhemodialysis, gender, and through each of the time points.

FIG. 11 shows the results of Peak Analysis of a Control Sample. Thecontrol sample shows no peaks characteristic of urea or glucose. FIG. 12shows the result of peak analysis for all spectra obtained from the sixdialysis patients at the three time points relative to control. Theannotated spectra in FIGS. 11 and 12 show the peaks characteristic forcarbohydrates, urea, sodium bicarbonate, and glucose/fattyacids/triglycerides. FIG. 13 shows the results of Peak Analysis for anindividual patient (Patient 4) over the three time points.

FIGS. 14-20 show the kinetics of different target analyte clearancesover the three time points during the hemodialysis treatment for each ofthe six patients, with FIGS. 14A and 14B comparing urea clearances withthat of creatinine, FIG. 15 showing carbohydrate modulation, FIG. 16showing low MW triglyceride clearances, FIG. 17 showing glycinesecretion, FIG. 18 showing aspartic acid/asparagine modulation, FIG. 19showing glutamic acid/glutamine modulation, and FIG. 20 showing sodiumbicarbonate secretion. A takeaway point from these figures is that theclearance patterns of the target analytes over the three time pointsdiffered between patients and were thus highly patient-specific.

FIG. 21 shows that the above chemometric (or “fingerprint”) analysis ofRaman spectra showed that the six patients have differentstarting/finish points and paths. FIG. 22 shows just the finishingpoints, indicating that the patient's kinetics tend to converge towardthe healthy control, yet all six patients finished treatment with adifferent physiology.

In summary, the results of the Example show that 1) the presence ofmultiple, diverse target analytes can be measured with Ramanspectrometry from dialysate from hemodialysis patients over a course ofa dialysis treatment, 2) the kinetics of these analytes over treatmentcan be followed, 3) the kinetics tends to be patient-specific indicatingphysiological differences between patients over the course of treatment,4) the kinetics tend to converge toward a healthy control, althoughphysiological differences remained at the end of treatment. Thus, theresults show that measuring target analytes in the dialysate ofindividual patients can be used to inform patient-specific dialysisprescriptions based on the kinetics of target analytes. For example, thekinetics of individual target analytes may suggest 1) longer or shorterdialysis treatment times, 2) increasing or decreasing the rate of bloodflow through the dialyzer, and/or 3) increasing or decreasing thefrequency of dialysis treatments. A skilled artisan (e.g. expertclinician) would be able to determine a patient-specific dialysisprescription based on the kinetics of the target analytes.

The present invention has been described with reference to particularembodiments having various features. In light of the disclosure providedabove, it will be apparent to those skilled in the art that variousmodifications and variations can be made in the practice of the presentinvention without departing from the scope or spirit of the invention.One skilled in the art will recognize that the disclosed features may beused singularly, in any combination, or omitted based on therequirements and specifications of a given application or design. Whenan embodiment refers to “comprising” certain features, it is to beunderstood that the embodiments can alternatively “consist of” or“consist essentially of” any one or more of the features. Otherembodiments of the invention will be apparent to those skilled in theart from consideration of the specification and practice of theinvention.

It is noted in particular that where a range of values is provided inthis specification, each value between the upper and lower limits ofthat range is also specifically disclosed. The upper and lower limits ofthese smaller ranges may independently be included or excluded in therange as well. The singular forms “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise. It is intendedthat the specification and examples be considered as exemplary in natureand that variations that do not depart from the essence of the inventionfall within the scope of the invention. Further, all of the referencescited in this disclosure are each individually incorporated by referenceherein in their entireties and as such are intended to provide anefficient way of supplementing the enabling disclosure of this inventionas well as provide background detailing the level of ordinary skill inthe art.

1. A method for determining dialysis treatment efficacy, the methodcomprising: providing a Raman spectrum of analytes present in a patientsample associated with a dialysis patient; providing one or more Ramanspectrum of analytes present in one or more reference sample associatedwith the dialysis patient and/or associated with other dialysispatients; comparing the Raman spectrum of the dialysis patient with theone or more reference samples to determine whether an analyte pattern oftwo or more select analytes is present in both; and based on thecomparing, determining disease state status of the patient.
 2. Themethod of claim 1, wherein the analyte pattern comprises two or moreanalytes chosen from creatinine, uric acid, uric acid based compounds,interleukin-8, bilirubin, a1-anti-trypsin, arginine, homoarginine, urea,urea-based compounds, urea nitrogen based compounds, ammonium-basedcompounds, nitrogen-based compounds, vitamins, vitamin C, vitamin B12,folic acid, zinc, amino acids, proteins, nucleic acids, pharmaceuticalcompounds, 2-heptanal, 2-hexenal, 2-nonenal, 4-decenal, 4-HO-decenal,4-HO-hexenal, 4-HO-nonenal, 4-HO-octenal,4-pyrididone-3-carboxy-1-β-D-ribonucleo side,8-Hydroxy-2′deoxyguanosine, α-Keto-δ-guanidinovlaeric acid, antranilicacid, argininic acid, dimethylarginine, cysteine, decanal,dimethylamine, ethylamine, guanidine, guanidinoacetic acid, guanidinesuccinic acid, hepatanal, hexanal, hypoxanthine, malondialdehyde,methylguanidine, monomethylamine, neopterine, nicotinamide,N-methyl-2-pyridone-5-carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, dimethylarginine,trimethylamine, trimethylamine-N-oxide,3-carboxy-4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-β-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, thiocyanate, α1-acid glycoprotein,α1-microglobulin, β-trace protein, β-microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, reduced glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, hemoglobin, myoglobin, osteocalcin, parathyroid hormone(PTH), prolactin, resistin, retinal binding protein, solubleintracellular adhesion molecule-1, TNF-α, and vascular endothelialgrowth factor.
 3. The method of claim 1, further comprising determiningdifferences between the analyte patterns of the Raman spectrum of thereference and patient samples to determine extent of the disease state.4. The method of claim 1, wherein the one or more reference sample iscollected at a time previous to collection of the patient sample.
 5. Themethod of claim 4, wherein the one or more reference sample isassociated with the dialysis patient.
 6. The method of claim 4, whereinthe one or more reference sample is associated with other dialysispatients.
 7. The method of claim 4, wherein the one or more referencesample includes one or more reference sample associated with thedialysis patient and one or more reference sample associated with otherdialysis patients.
 8. The method of claim 1, wherein the patient sampleis a dialysate, blood, plasma, or urine. 9-12. (canceled)
 13. A methodfor monitoring the health of a dialysis patient, the method comprising:receiving spectra from a Raman spectrometer of one or more targetanalytes obtained from a first sample from a dialysis patient at a firsttime point; storing the spectra in memory; receiving spectra from aRaman spectrometer of one or more target analytes obtained from a secondsample from the dialysis patient at a second later time point; comparingthe Raman spectra from the second time point with the Raman spectra fromthe first time point with a processor; and determining a dialysistreatment outcome characteristic of the dialysis patient based on thecomparison. 14-22. (canceled)
 23. The method of claim 13, wherein thesample is a dialysate, waste dialysate, perfusate, a body fluid, blood,plasma, feces, urine, cerebrospinal fluid, or saliva. 24-26. (canceled)27. The method of claim 13, wherein the target analytes are chosen fromone or more of the following: creatinine, uric acid, uric acid basedcompounds, interleukin-8, bilirubin, a1-anti-trypsin, arginine,homoarginine, urea, urea-based compounds, urea nitrogen based compounds,ammonium-based compounds, nitrogen-based compounds, vitamins, vitamin C,vitamin B12, folic acid, zinc, amino acids, proteins, nucleic acids,pharmaceutical compounds, 2-heptanal, 2-hexenal, 2-nonenal, 4-decenal,4-HO-decenal, 4-HO-hexenal, 4-HO-nonenal, 4-HO-octenal,4-pyrididone-3-carboxy-1β-D-ribonucleoside, 8-Hydroxy-2′ deoxyguanosine,α-Keto-δ-guanidinovlaeric acid, antranilic acid, argininic acid,dimethylarginine, cysteine, decanal, dimethylamine, ethylamine,guanidine, guanidinoacetic acid, guanidine succinic acid, hepatanal,hexanal, hypoxanthine, malondialdehyde, methylguanidine,monomethylamine, neopterine, nicotinamide,N-methyl-2-pyridone-5-carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, dimethylarginine,trimethylamine, trimethylamine-N-oxide,3-carboxy-4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-δ-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, thiocyanate, α1-acid glycoprotein,α1-microglobulin, δ-trace protein, β-microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, reduced glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, hemoglobin, myoglobin, osteocalcin, parathyroid hormone(PTH), prolactin, resistin, retinal binding protein, solubleintracellular adhesion molecule-1, TNF-α, and vascular endothelialgrowth factor.
 28. The method of claim 13, wherein the dialysis ishemodialysis.
 29. The method of claim 13, wherein the dialysis isperitoneal dialysis.
 30. The method of claim 13, wherein the analytesare chosen from one or more of urea toxins, nutritional factors,electrolytes, and pharmaceutical compounds.
 31. The method of claim 13,wherein the dialysis treatment outcome characteristic is based on thekinetics of the one or more target analytes. 32-36. (canceled)
 37. Amethod of determining a patient-specific dialysis prescription,comprising: subjecting a patient to a dialysis treatment based on afirst dialysis prescription; during the dialysis treatment, takingmultiple samples associated with the patient; measuring a Raman spectrumof analytes present in the samples; determining the kinetics of theanalytes based on the Raman spectrum; and modifying the first dialysisprescription based on the kinetics of the analytes to produce a seconddialysis prescription that is patient-specific.
 38. The method of claim37, wherein the samples are taken at the beginning, middle, and end ofthe dialysis treatment and are dialysate samples.
 39. The method ofclaim 37, wherein the analytes are chosen from one or more ofcreatinine, uric acid, uric acid based compounds, interleukin-8,bilirubin, a1-anti-trypsin, arginine, homoarginine, urea, urea-basedcompounds, urea nitrogen based compounds, ammonium-based compounds,nitrogen-based compounds, vitamins, vitamin C, vitamin B12, folic acid,zinc, amino acids, proteins, nucleic acids, pharmaceutical compounds,2-heptanal, 2-hexenal, 2-nonenal, 4-decenal, 4-HO-decenal, 4-HO-hexenal,4-HO-nonenal, 4-HO-octenal, 4 pyrididone-3-carboxy-1-β-D-ribonucleoside,8-Hydroxy-2′ deoxyguano sine, α Keto δ guanidinovlaeric acid, antranilicacid, argininic acid, dimethylarginine, cysteine, decanal,dimethylamine, ethylamine, guanidine, guanidinoacetic acid, guanidinesuccinic acid, hepatanal, hexanal, hypoxanthine, malondialdehyde,methylguanidine, monomethylamine, neopterine, nicotinamide, Nmethyl-2-pyridone-5-carboxamide, N-methyl-4-pyridone-3-carboxamide,nonanal, noradrenaline, oxalate, phenylacetic acid, dimethylarginine,trimethylamine, trimethylamine-N-oxide, 3carboxy-4-methyl-5-propyl-2-furan-propanoic acid, acrolein,carboxymethyllysine, dihydroxyphenylalanine, hippuric acid,homocysteine, indicant, indole-3-acetic acid, indoxyl sulfate,indoxyl-β-D-glucoronide, kynurenic acid, p-cresyl sulfate, pentosidine,phenol, putrescine, permidine, thiocyanate, α1-acid glycoprotein,α1-microglobulin, β-trace protein, β microglobulin, adiponectin,angiogenin, calcitonin, complement factor D, cysstain C, fibroblastgrowth factor-23, glutathione, reduced glutathione, IGF-1, IL-6, IL-8,IL-10, leptin, myoglobin, osteocalcin, parathyroid hormone (PTH),prolactin, resistin, retinal binding protein, soluble intracellularadhesion molecule-1, TNF-α, and vascular endothelial growth factor. 40.The method of claim 37, wherein the samples are a dialysate, bloodplasma, or urine. 41-43. (canceled)
 44. The method of claim 37, whereinmodifying the first dialysis prescription involves longer or shorterdialysis treatment times, increasing or decreasing the rate of bloodflow through a dialyzer, or increasing or decreasing the frequency ofdialysis treatments.